Table 11 Regression results equation 3, interaction between the migrant condition and theoretical explanations of overeducation
From: Skill mismatch among migrant workers: evidence from a large multi-country dataset
(Interaction) | (Interaction) | |||||
|---|---|---|---|---|---|---|
Estimate coeff. | Std. | Estimate coeff. | Std. | |||
Age | −0.0133 | *** | 0.00147 | −0.0068 | *** | 0.0004 |
Breaks | 0.0946 | *** | 0.00936 | 0.0726 | *** | 0.0025 |
Gender (female = 1) | 0.1981 | *** | 0.0276 | 0.1489 | *** | 0.008 |
Job prospects | −0.9032 | *** | 0.02802 | |||
MIGRANT | −1.8293 | * | 0.04494 | −0.0914 | 0.0716 | |
Age * Migrant | 0.0193 | *** | 0.00074 | 0.0094 | *** | 0.0015 |
Breaks * Migrant | −0.0529 | * | 0.03251 | −0.0116 | 0.0086 | |
Gender * Migrant | −0.2068 | 0.18685 | −0.0234 | 0.03 | ||
Language mismatch | 0.0894 | 0.68452 | 0.0894 | * | 0.0375 | |
Job prosp. * Migrant | 0.1456 | . | 0.06303 | |||
Continent (ref. EU15) | ||||||
EU12 | −0.0955 | * | 0.04325 | 0.0507 | ** | 0.0188 |
Africa | −0.0565 | 0.06962 | −0.0190 | 0.0194 | ||
Latin America | 0.0860 | * | 0.03355 | 0.2090 | *** | 0.0118 |
Asia | 0.5055 | *** | 0.10587 | 0.2676 | *** | 0.0144 |
North America and Oceania | 0.3725 | * | 0.17095 | 0.0819 | 0.0377 | |
Europe non-EU | 0.4115 | *** | 0.04667 | 0.5254 | *** | 0.0115 |
Continent (ref. EU15) * Migrant | ||||||
EU12 | 0.0324 | 0.19144 | −0.0614 | 0.088 | ||
Africa | −0.1048 | 0.22151 | −0.4024 | *** | 0.0665 | |
Latin America | −0.1872 | 0.19152 | −0.3391 | *** | 0.0611 | |
Asia | −0.7032 | . | 0.3797 | 0.0042 | 0.0608 | |
North America and Oceania | −0.8123 | * | 0.38313 | −0.4641 | *** | 0.0872 |
Europe non-EU | −0.2827 | . | 0.16117 | −0.1813 | *** | 0.0425 |
Education (ref. ISCED10) * Migrant | ||||||
ISCED 20 | 0.5112 | * | 0.6938 | −0.1934 | 0.157 | |
ISCED 30 | 0.2788 | ** | 0.68213 | −0.1789 | * | 0.0712 |
ISCED 40 | 0.3750 | . | 0.71559 | 0.0670 | 0.041 | |
ISCED 50 | 0.3062 | * | 0.67516 | −0.1529 | * | 0.0608 |
ISCED 60 | 0.7363 | . | 0.70176 | −0.0431 | 0.0583 | |
Corporate hierarchy * Migrant | ||||||
CH2 | 1.2925 | 0.64551 | −0.0313 | 0.1381 | ||
CH3 | 2.0681 | 0.72598 | 0.2629 | ** | 0.0948 | |
CH4 | 1.3311 | 0.68186 | 0.0012 | 0.0682 | ||
CH5 | 1.3178 | 0.66506 | 0.1043 | 0.0754 | ||
CH6 | 1.3776 | 0.72548 | −0.1036 | 0.1524 | ||
Firm size (ref. 1–10) * Migrant | ||||||
size 11-50 | −0.1232 | 0.15772 | 0.0259 | 0.0448 | ||
size 51-100 | 0.3917 | * | 0.18919 | −0.0176 | 0.0516 | |
size 101-500 | −0.0166 | 0.16223 | 0.0432 | 0.0432 | ||
size 500+ | 0.0019 | 0.16801 | −0.0836 | . | 0.0436 | |
Industry (ref. agr., man. and constr.) * Migrant | ||||||
Trade, transp. and hotels | 0.0572 | 0.19539 | ||||
Commercial serv. | −0.1712 | 0.16287 | ||||
Education level | YES | |||||
Corporate hierarchy | YES | |||||
Firm size | YES | |||||
Industry | YES | |||||
Nagelkerke presudo R2 | 0.099 | 0.035 | ||||
LR chi2 | 2,435.13 | 8,843.44 | ||||
Pr(> chi2) | <0.0001 | <0.0001 | ||||